使用B细胞和T细胞受体序列的机器学习进行疾病诊断
Maxim E Zaslavsky1, Erin Craig2, Jackson K Michuda2
1Department of Computer Science, Stanford University, Stanford, CA, USA.
Science (New York, N.Y.)
|February 20, 2025
概括
这项研究引入了用于免疫诊断的机器学习 (MILAD),一种使用免疫受体数据查疾病的新框架. MILAD可以检测感染,自身免疫性疾病和疫苗反应,从而提高诊断能力.
科学领域:
- 免疫学
- 计算生物学
- 医疗诊断
背景情况:
- 目前的临床诊断依赖于体检,病史,实验室测试和成像,通常忽视免疫系统对抗原暴露的记忆.
- B细胞和T细胞受体编码了个体与抗原接触的详细历史, 代表了大部分未开发的诊断资源.
研究的目的:
- 开发一种解释框架,即用于免疫诊断的机器学习 (MILAD),用于同时或精确地查多种疾病.
- 利用免疫受体数据来提高疾病的检测和特征.
主要方法:
- 对593个人的免疫受体数据集的分析.
- 开发用于免疫诊断的机器学习模型.
- 通过与已知的免疫反应进行对模型解释性的验证.
主要成果:
- MILAD框架成功选多种疾病,包括特定的感染 (例如,SARS-CoV-2,流感,艾滋病毒),自身免疫性疾病 (例如,狼,1型糖尿病) 和疫苗反应.
- 该模型可以识别不同疾病的不同免疫特征,并量化疾病严重程度的差异.
- 该模型的可解释特征与已知的免疫学知识保持一致,并突出显示抗原特异性受体.
结论:
- 通过利用免疫系统的受体数据,机器学习为免疫诊断提供了一种新的临床诊断方法.
- 这种框架对免疫反应的科学和临床解释具有广泛的潜力,使同时或有针对性的疾病查成为可能.
- 通过免疫受体分析,MILAD通过发现特定的感染,自身免疫性疾病和疫苗疗效来提高诊断能力.
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